Overview

Dataset statistics

Number of variables21
Number of observations6599
Missing cells1246
Missing cells (%)0.9%
Duplicate rows595
Duplicate rows (%)9.0%
Total size in memory1.1 MiB
Average record size in memory168.0 B

Variable types

Numeric14
Categorical7

Alerts

Dataset has 595 (9.0%) duplicate rowsDuplicates
HomePage is highly overall correlated with ProductDescriptionPage_DurationHigh correlation
HomePage_Duration is highly overall correlated with LandingPage and 3 other fieldsHigh correlation
LandingPage is highly overall correlated with HomePage_Duration and 2 other fieldsHigh correlation
LandingPage_Duration is highly overall correlated with HomePage_Duration and 3 other fieldsHigh correlation
ProductDescriptionPage is highly overall correlated with HomePage_Duration and 2 other fieldsHigh correlation
ProductDescriptionPage_Duration is highly overall correlated with HomePage and 4 other fieldsHigh correlation
GoogleMetric:Bounce Rates is highly overall correlated with GoogleMetric:Exit RatesHigh correlation
GoogleMetric:Exit Rates is highly overall correlated with GoogleMetric:Bounce RatesHigh correlation
OS is highly overall correlated with SearchEngine and 1 other fieldsHigh correlation
SearchEngine is highly overall correlated with OS and 1 other fieldsHigh correlation
CustomerType is highly overall correlated with OS and 1 other fieldsHigh correlation
LandingPage_Duration has 75 (1.1%) missing valuesMissing
GoogleMetric:Bounce Rates has 66 (1.0%) missing valuesMissing
Type of Traffic has 68 (1.0%) missing valuesMissing
CustomerType has 88 (1.3%) missing valuesMissing
Education has 69 (1.0%) missing valuesMissing
HomePage has 3152 (47.8%) zerosZeros
HomePage_Duration has 3222 (48.8%) zerosZeros
LandingPage has 5166 (78.3%) zerosZeros
LandingPage_Duration has 5271 (79.9%) zerosZeros
ProductDescriptionPage_Duration has 398 (6.0%) zerosZeros
GoogleMetric:Bounce Rates has 2920 (44.2%) zerosZeros
GoogleMetric:Page Values has 5319 (80.6%) zerosZeros
SeasonalPurchase has 5858 (88.8%) zerosZeros

Reproduction

Analysis started2023-01-21 18:37:16.539899
Analysis finished2023-01-21 18:37:48.986481
Duration32.45 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

HomePage
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)0.4%
Missing51
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.2832926
Minimum0
Maximum26
Zeros3152
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:49.081859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3453869
Coefficient of variation (CV)1.4651591
Kurtosis5.2270942
Mean2.2832926
Median Absolute Deviation (MAD)1
Skewness2.0322595
Sum14951
Variance11.191613
MonotonicityNot monotonic
2023-01-22T00:07:49.224701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3152
47.8%
1 675
 
10.2%
2 601
 
9.1%
3 476
 
7.2%
4 373
 
5.7%
5 282
 
4.3%
6 216
 
3.3%
7 200
 
3.0%
8 166
 
2.5%
9 131
 
2.0%
Other values (16) 276
 
4.2%
ValueCountFrequency (%)
0 3152
47.8%
1 675
 
10.2%
2 601
 
9.1%
3 476
 
7.2%
4 373
 
5.7%
5 282
 
4.3%
6 216
 
3.3%
7 200
 
3.0%
8 166
 
2.5%
9 131
 
2.0%
ValueCountFrequency (%)
26 1
 
< 0.1%
24 4
 
0.1%
23 3
 
< 0.1%
22 1
 
< 0.1%
21 2
 
< 0.1%
20 2
 
< 0.1%
19 2
 
< 0.1%
18 9
0.1%
17 6
 
0.1%
16 15
0.2%

HomePage_Duration
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1745
Distinct (%)26.7%
Missing55
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean76.994609
Minimum0
Maximum3398.75
Zeros3222
Zeros (%)48.8%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:49.341247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.875
Q391
95-th percentile335.12917
Maximum3398.75
Range3398.75
Interquartile range (IQR)91

Descriptive statistics

Standard deviation168.6797
Coefficient of variation (CV)2.1907988
Kurtosis63.76094
Mean76.994609
Median Absolute Deviation (MAD)4.875
Skewness6.0446373
Sum503852.72
Variance28452.84
MonotonicityNot monotonic
2023-01-22T00:07:49.465383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3222
48.8%
5 36
 
0.5%
4 28
 
0.4%
6 26
 
0.4%
7 25
 
0.4%
15 20
 
0.3%
21 19
 
0.3%
14 19
 
0.3%
20 18
 
0.3%
11 17
 
0.3%
Other values (1735) 3114
47.2%
(Missing) 55
 
0.8%
ValueCountFrequency (%)
0 3222
48.8%
2 4
 
0.1%
3 13
 
0.2%
3.5 2
 
< 0.1%
4 28
 
0.4%
4.333333333 1
 
< 0.1%
4.75 2
 
< 0.1%
5 36
 
0.5%
5.384615385 1
 
< 0.1%
5.5 5
 
0.1%
ValueCountFrequency (%)
3398.75 1
 
< 0.1%
2720.5 1
 
< 0.1%
2629.253968 1
 
< 0.1%
2156.166667 1
 
< 0.1%
1660.3 1
 
< 0.1%
1652 3
< 0.1%
1640.590909 1
 
< 0.1%
1592.916667 1
 
< 0.1%
1561.717567 1
 
< 0.1%
1559.75 1
 
< 0.1%

LandingPage
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.2%
Missing56
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.49472719
Minimum0
Maximum24
Zeros5166
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:49.575656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2725236
Coefficient of variation (CV)2.5721724
Kurtosis34.795915
Mean0.49472719
Median Absolute Deviation (MAD)0
Skewness4.4152555
Sum3237
Variance1.6193164
MonotonicityNot monotonic
2023-01-22T00:07:49.678438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 5166
78.3%
1 539
 
8.2%
2 408
 
6.2%
3 186
 
2.8%
4 112
 
1.7%
5 47
 
0.7%
6 38
 
0.6%
7 19
 
0.3%
9 10
 
0.2%
8 8
 
0.1%
Other values (5) 10
 
0.2%
(Missing) 56
 
0.8%
ValueCountFrequency (%)
0 5166
78.3%
1 539
 
8.2%
2 408
 
6.2%
3 186
 
2.8%
4 112
 
1.7%
5 47
 
0.7%
6 38
 
0.6%
7 19
 
0.3%
8 8
 
0.1%
9 10
 
0.2%
ValueCountFrequency (%)
24 1
 
< 0.1%
16 1
 
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%
10 4
 
0.1%
9 10
 
0.2%
8 8
 
0.1%
7 19
0.3%
6 38
0.6%
5 47
0.7%

LandingPage_Duration
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct667
Distinct (%)10.2%
Missing75
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean32.90776
Minimum0
Maximum2549.375
Zeros5271
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:49.792222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile186.5
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation134.68331
Coefficient of variation (CV)4.0927524
Kurtosis86.044213
Mean32.90776
Median Absolute Deviation (MAD)0
Skewness7.8483134
Sum214690.23
Variance18139.595
MonotonicityNot monotonic
2023-01-22T00:07:49.933494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5271
79.9%
9 19
 
0.3%
7 16
 
0.2%
10 16
 
0.2%
6 15
 
0.2%
13 13
 
0.2%
12 12
 
0.2%
4 12
 
0.2%
11 12
 
0.2%
5 11
 
0.2%
Other values (657) 1127
 
17.1%
(Missing) 75
 
1.1%
ValueCountFrequency (%)
0 5271
79.9%
1 4
 
0.1%
2 7
 
0.1%
3 11
 
0.2%
3.5 1
 
< 0.1%
4 12
 
0.2%
5 11
 
0.2%
5.5 3
 
< 0.1%
6 15
 
0.2%
6.333333333 1
 
< 0.1%
ValueCountFrequency (%)
2549.375 1
< 0.1%
2256.916667 1
< 0.1%
2252.033333 1
< 0.1%
2050.433333 1
< 0.1%
1830.5 1
< 0.1%
1530 1
< 0.1%
1494.5 1
< 0.1%
1475.25 2
< 0.1%
1467 1
< 0.1%
1439 1
< 0.1%

ProductDescriptionPage
Real number (ℝ)

Distinct242
Distinct (%)3.7%
Missing58
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean30.742088
Minimum0
Maximum705
Zeros27
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:50.068340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median17
Q337
95-th percentile106
Maximum705
Range705
Interquartile range (IQR)30

Descriptive statistics

Standard deviation43.539549
Coefficient of variation (CV)1.4162847
Kurtosis41.853752
Mean30.742088
Median Absolute Deviation (MAD)12
Skewness4.8148943
Sum201084
Variance1895.6923
MonotonicityNot monotonic
2023-01-22T00:07:50.197249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 335
 
5.1%
2 263
 
4.0%
5 247
 
3.7%
3 239
 
3.6%
7 217
 
3.3%
4 210
 
3.2%
8 194
 
2.9%
6 189
 
2.9%
11 175
 
2.7%
10 175
 
2.7%
Other values (232) 4297
65.1%
ValueCountFrequency (%)
0 27
 
0.4%
1 335
5.1%
2 263
4.0%
3 239
3.6%
4 210
3.2%
5 247
3.7%
6 189
2.9%
7 217
3.3%
8 194
2.9%
9 168
2.5%
ValueCountFrequency (%)
705 1
< 0.1%
686 2
< 0.1%
534 1
< 0.1%
518 1
< 0.1%
449 1
< 0.1%
397 1
< 0.1%
377 1
< 0.1%
374 1
< 0.1%
358 1
< 0.1%
351 1
< 0.1%

ProductDescriptionPage_Duration
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct4536
Distinct (%)69.4%
Missing63
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1151.2311
Minimum0
Maximum63973.522
Zeros398
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:50.340670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1172.12778
median570.34161
Q31425.75
95-th percentile4164.0824
Maximum63973.522
Range63973.522
Interquartile range (IQR)1253.6222

Descriptive statistics

Standard deviation1917.2503
Coefficient of variation (CV)1.6653913
Kurtosis224.84039
Mean1151.2311
Median Absolute Deviation (MAD)479.34161
Skewness9.5514826
Sum7524446.8
Variance3675848.7
MonotonicityNot monotonic
2023-01-22T00:07:50.463531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 398
 
6.0%
12 14
 
0.2%
108 13
 
0.2%
22 12
 
0.2%
5 11
 
0.2%
11 10
 
0.2%
8 9
 
0.1%
24 9
 
0.1%
117 9
 
0.1%
57 9
 
0.1%
Other values (4526) 6042
91.6%
(Missing) 63
 
1.0%
ValueCountFrequency (%)
0 398
6.0%
1 1
 
< 0.1%
3 3
 
< 0.1%
4 6
 
0.1%
5 11
 
0.2%
6 3
 
< 0.1%
7 6
 
0.1%
8 9
 
0.1%
9 7
 
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
63973.52223 1
< 0.1%
43171.23338 1
< 0.1%
23342.08205 2
< 0.1%
18504.12621 1
< 0.1%
16138.2908 2
< 0.1%
14988.59151 1
< 0.1%
14577.08498 1
< 0.1%
14213.63652 1
< 0.1%
13717.40205 1
< 0.1%
13158.66667 1
< 0.1%

GoogleMetric:Bounce Rates
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct1056
Distinct (%)16.2%
Missing66
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.022392125
Minimum0
Maximum0.2
Zeros2920
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:50.592790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.003165468
Q30.017777778
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.017777778

Descriptive statistics

Standard deviation0.048373564
Coefficient of variation (CV)2.1602936
Kurtosis7.6964357
Mean0.022392125
Median Absolute Deviation (MAD)0.003165468
Skewness2.9350221
Sum146.28775
Variance0.0023400017
MonotonicityNot monotonic
2023-01-22T00:07:50.726589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2920
44.2%
0.2 369
 
5.6%
0.028571429 75
 
1.1%
0.066666667 73
 
1.1%
0.04 64
 
1.0%
0.05 64
 
1.0%
0.016666667 59
 
0.9%
0.025 52
 
0.8%
0.1 51
 
0.8%
0.0125 47
 
0.7%
Other values (1046) 2759
41.8%
(Missing) 66
 
1.0%
ValueCountFrequency (%)
0 2920
44.2%
2.73 × 10-52
 
< 0.1%
3.83 × 10-51
 
< 0.1%
8.08 × 10-51
 
< 0.1%
8.14 × 10-52
 
< 0.1%
9.83 × 10-51
 
< 0.1%
0.000123762 2
 
< 0.1%
0.00015674 1
 
< 0.1%
0.000183655 1
 
< 0.1%
0.000189394 1
 
< 0.1%
ValueCountFrequency (%)
0.2 369
5.6%
0.18 1
 
< 0.1%
0.176923077 1
 
< 0.1%
0.175 1
 
< 0.1%
0.166666667 1
 
< 0.1%
0.164230769 1
 
< 0.1%
0.155555556 2
 
< 0.1%
0.15 10
 
0.2%
0.146666667 1
 
< 0.1%
0.142857143 2
 
< 0.1%

GoogleMetric:Exit Rates
Real number (ℝ)

Distinct2390
Distinct (%)36.5%
Missing51
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.043874416
Minimum0
Maximum0.2
Zeros42
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:50.862678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0050078565
Q10.014542925
median0.026458438
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035457075

Descriptive statistics

Standard deviation0.048290348
Coefficient of variation (CV)1.1006494
Kurtosis3.9253359
Mean0.043874416
Median Absolute Deviation (MAD)0.014718033
Skewness2.1087999
Sum287.28968
Variance0.0023319577
MonotonicityNot monotonic
2023-01-22T00:07:50.992650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 372
 
5.6%
0.1 204
 
3.1%
0.05 182
 
2.8%
0.033333333 158
 
2.4%
0.066666667 148
 
2.2%
0.025 125
 
1.9%
0.04 115
 
1.7%
0.016666667 109
 
1.7%
0.02 96
 
1.5%
0.028571429 86
 
1.3%
Other values (2380) 4953
75.1%
ValueCountFrequency (%)
0 42
0.6%
0.000409836 2
 
< 0.1%
0.000446429 1
 
< 0.1%
0.000468384 1
 
< 0.1%
0.000505051 2
 
< 0.1%
0.000544218 2
 
< 0.1%
0.00070922 1
 
< 0.1%
0.000732601 1
 
< 0.1%
0.0008 2
 
< 0.1%
0.000826446 1
 
< 0.1%
ValueCountFrequency (%)
0.2 372
5.6%
0.192307692 1
 
< 0.1%
0.186666667 1
 
< 0.1%
0.183333333 1
 
< 0.1%
0.181818182 1
 
< 0.1%
0.18034188 1
 
< 0.1%
0.18 1
 
< 0.1%
0.177777778 1
 
< 0.1%
0.175 5
 
0.1%
0.16875 1
 
< 0.1%

GoogleMetric:Page Values
Real number (ℝ)

Distinct1098
Distinct (%)16.8%
Missing45
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean4.9821082
Minimum0
Maximum361.76374
Zeros5319
Zeros (%)80.6%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:51.128736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile32.666943
Maximum361.76374
Range361.76374
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.635847
Coefficient of variation (CV)3.5398362
Kurtosis76.524866
Mean4.9821082
Median Absolute Deviation (MAD)0
Skewness7.0332943
Sum32652.737
Variance311.0231
MonotonicityNot monotonic
2023-01-22T00:07:51.253094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5319
80.6%
64.4161607 3
 
< 0.1%
4.495585092 3
 
< 0.1%
67.39709637 3
 
< 0.1%
53.988 3
 
< 0.1%
10.41358118 3
 
< 0.1%
6.402612903 3
 
< 0.1%
24.13073118 3
 
< 0.1%
3.170426459 3
 
< 0.1%
1.725866353 2
 
< 0.1%
Other values (1088) 1209
 
18.3%
(Missing) 45
 
0.7%
ValueCountFrequency (%)
0 5319
80.6%
0.038034542 1
 
< 0.1%
0.067049546 1
 
< 0.1%
0.093546949 1
 
< 0.1%
0.120699914 1
 
< 0.1%
0.129676893 1
 
< 0.1%
0.139200623 1
 
< 0.1%
0.150650498 2
 
< 0.1%
0.152167439 1
 
< 0.1%
0.154821253 1
 
< 0.1%
ValueCountFrequency (%)
361.7637419 1
< 0.1%
261.4912857 1
< 0.1%
258.5498732 2
< 0.1%
239.98 1
< 0.1%
226.6777017 1
< 0.1%
215.0094118 1
< 0.1%
204.0079491 1
< 0.1%
177.7714536 1
< 0.1%
173.4697917 1
< 0.1%
166.3735531 1
< 0.1%

SeasonalPurchase
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing45
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.064296613
Minimum0
Maximum1
Zeros5858
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:51.361114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20267756
Coefficient of variation (CV)3.1522277
Kurtosis9.116994
Mean0.064296613
Median Absolute Deviation (MAD)0
Skewness3.1903735
Sum421.4
Variance0.041078194
MonotonicityNot monotonic
2023-01-22T00:07:51.450558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5858
88.8%
0.6 204
 
3.1%
0.8 187
 
2.8%
0.4 126
 
1.9%
0.2 100
 
1.5%
1 79
 
1.2%
(Missing) 45
 
0.7%
ValueCountFrequency (%)
0 5858
88.8%
0.2 100
 
1.5%
0.4 126
 
1.9%
0.6 204
 
3.1%
0.8 187
 
2.8%
1 79
 
1.2%
ValueCountFrequency (%)
1 79
 
1.2%
0.8 187
 
2.8%
0.6 204
 
3.1%
0.4 126
 
1.9%
0.2 100
 
1.5%
0 5858
88.8%
Distinct10
Distinct (%)0.2%
Missing58
Missing (%)0.9%
Memory size51.7 KiB
May
1788 
Nov
1574 
Mar
1009 
Dec
956 
Oct
284 
Other values (5)
930 

Length

Max length4
Median length3
Mean length3.0226265
Min length3

Characters and Unicode

Total characters19771
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFeb
2nd rowFeb
3rd rowFeb
4th rowFeb
5th rowFeb

Common Values

ValueCountFrequency (%)
May 1788
27.1%
Nov 1574
23.9%
Mar 1009
15.3%
Dec 956
14.5%
Oct 284
 
4.3%
Jul 239
 
3.6%
Sep 220
 
3.3%
Aug 206
 
3.1%
June 148
 
2.2%
Feb 117
 
1.8%
(Missing) 58
 
0.9%

Length

2023-01-22T00:07:51.550022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T00:07:51.993334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
may 1788
27.3%
nov 1574
24.1%
mar 1009
15.4%
dec 956
14.6%
oct 284
 
4.3%
jul 239
 
3.7%
sep 220
 
3.4%
aug 206
 
3.1%
june 148
 
2.3%
feb 117
 
1.8%

Most occurring characters

ValueCountFrequency (%)
M 2797
14.1%
a 2797
14.1%
y 1788
9.0%
N 1574
8.0%
o 1574
8.0%
v 1574
8.0%
e 1441
7.3%
c 1240
6.3%
r 1009
 
5.1%
D 956
 
4.8%
Other values (12) 3021
15.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13230
66.9%
Uppercase Letter 6541
33.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2797
21.1%
y 1788
13.5%
o 1574
11.9%
v 1574
11.9%
e 1441
10.9%
c 1240
9.4%
r 1009
 
7.6%
u 593
 
4.5%
t 284
 
2.1%
l 239
 
1.8%
Other values (4) 691
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
M 2797
42.8%
N 1574
24.1%
D 956
 
14.6%
J 387
 
5.9%
O 284
 
4.3%
S 220
 
3.4%
A 206
 
3.1%
F 117
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 19771
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 2797
14.1%
a 2797
14.1%
y 1788
9.0%
N 1574
8.0%
o 1574
8.0%
v 1574
8.0%
e 1441
7.3%
c 1240
6.3%
r 1009
 
5.1%
D 956
 
4.8%
Other values (12) 3021
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 2797
14.1%
a 2797
14.1%
y 1788
9.0%
N 1574
8.0%
o 1574
8.0%
v 1574
8.0%
e 1441
7.3%
c 1240
6.3%
r 1009
 
5.1%
D 956
 
4.8%
Other values (12) 3021
15.3%

OS
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing62
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2.1340064
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:52.098073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90933974
Coefficient of variation (CV)0.42611856
Kurtosis9.7533159
Mean2.1340064
Median Absolute Deviation (MAD)0
Skewness1.9579218
Sum13950
Variance0.82689877
MonotonicityNot monotonic
2023-01-22T00:07:52.185876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 3454
52.3%
3 1400
21.2%
1 1361
 
20.6%
4 263
 
4.0%
8 39
 
0.6%
6 11
 
0.2%
5 6
 
0.1%
7 3
 
< 0.1%
(Missing) 62
 
0.9%
ValueCountFrequency (%)
1 1361
 
20.6%
2 3454
52.3%
3 1400
21.2%
4 263
 
4.0%
5 6
 
0.1%
6 11
 
0.2%
7 3
 
< 0.1%
8 39
 
0.6%
ValueCountFrequency (%)
8 39
 
0.6%
7 3
 
< 0.1%
6 11
 
0.2%
5 6
 
0.1%
4 263
 
4.0%
3 1400
21.2%
2 3454
52.3%
1 1361
 
20.6%

SearchEngine
Real number (ℝ)

Distinct13
Distinct (%)0.2%
Missing58
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2.3577435
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:52.280133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7213749
Coefficient of variation (CV)0.73009423
Kurtosis12.619679
Mean2.3577435
Median Absolute Deviation (MAD)0
Skewness3.2473353
Sum15422
Variance2.9631315
MonotonicityNot monotonic
2023-01-22T00:07:52.376832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 4266
64.6%
1 1283
 
19.4%
4 376
 
5.7%
5 239
 
3.6%
10 88
 
1.3%
6 86
 
1.3%
8 75
 
1.1%
3 55
 
0.8%
7 30
 
0.5%
13 30
 
0.5%
Other values (3) 13
 
0.2%
(Missing) 58
 
0.9%
ValueCountFrequency (%)
1 1283
 
19.4%
2 4266
64.6%
3 55
 
0.8%
4 376
 
5.7%
5 239
 
3.6%
6 86
 
1.3%
7 30
 
0.5%
8 75
 
1.1%
9 1
 
< 0.1%
10 88
 
1.3%
ValueCountFrequency (%)
13 30
 
0.5%
12 6
 
0.1%
11 6
 
0.1%
10 88
 
1.3%
9 1
 
< 0.1%
8 75
 
1.1%
7 30
 
0.5%
6 86
 
1.3%
5 239
3.6%
4 376
5.7%

Zone
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing47
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3.1846764
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:52.467211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4073281
Coefficient of variation (CV)0.75590978
Kurtosis-0.20576168
Mean3.1846764
Median Absolute Deviation (MAD)2
Skewness0.95446905
Sum20866
Variance5.7952284
MonotonicityNot monotonic
2023-01-22T00:07:52.558427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 2495
37.8%
3 1295
19.6%
4 648
 
9.8%
2 580
 
8.8%
6 444
 
6.7%
7 398
 
6.0%
9 270
 
4.1%
8 248
 
3.8%
5 174
 
2.6%
(Missing) 47
 
0.7%
ValueCountFrequency (%)
1 2495
37.8%
2 580
 
8.8%
3 1295
19.6%
4 648
 
9.8%
5 174
 
2.6%
6 444
 
6.7%
7 398
 
6.0%
8 248
 
3.8%
9 270
 
4.1%
ValueCountFrequency (%)
9 270
 
4.1%
8 248
 
3.8%
7 398
 
6.0%
6 444
 
6.7%
5 174
 
2.6%
4 648
 
9.8%
3 1295
19.6%
2 580
 
8.8%
1 2495
37.8%

Type of Traffic
Real number (ℝ)

Distinct17
Distinct (%)0.3%
Missing68
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean4.0419538
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.7 KiB
2023-01-22T00:07:52.659567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0277465
Coefficient of variation (CV)0.99648505
Kurtosis3.5991598
Mean4.0419538
Median Absolute Deviation (MAD)1
Skewness1.9894387
Sum26398
Variance16.222742
MonotonicityNot monotonic
2023-01-22T00:07:52.755902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 2032
30.8%
1 1349
20.4%
3 1111
16.8%
4 537
 
8.1%
13 388
 
5.9%
6 250
 
3.8%
10 216
 
3.3%
8 176
 
2.7%
5 146
 
2.2%
11 141
 
2.1%
Other values (7) 185
 
2.8%
ValueCountFrequency (%)
1 1349
20.4%
2 2032
30.8%
3 1111
16.8%
4 537
 
8.1%
5 146
 
2.2%
6 250
 
3.8%
7 20
 
0.3%
8 176
 
2.7%
9 15
 
0.2%
10 216
 
3.3%
ValueCountFrequency (%)
20 106
 
1.6%
19 12
 
0.2%
18 5
 
0.1%
15 20
 
0.3%
14 7
 
0.1%
13 388
5.9%
11 141
 
2.1%
10 216
3.3%
9 15
 
0.2%
8 176
2.7%

CustomerType
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing88
Missing (%)1.3%
Memory size51.7 KiB
Returning_Visitor
5585 
New_Visitor
891 
Other
 
35

Length

Max length17
Median length17
Mean length16.114422
Min length5

Characters and Unicode

Total characters104921
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturning_Visitor
2nd rowReturning_Visitor
3rd rowReturning_Visitor
4th rowReturning_Visitor
5th rowReturning_Visitor

Common Values

ValueCountFrequency (%)
Returning_Visitor 5585
84.6%
New_Visitor 891
 
13.5%
Other 35
 
0.5%
(Missing) 88
 
1.3%

Length

2023-01-22T00:07:52.867144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T00:07:52.968157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
returning_visitor 5585
85.8%
new_visitor 891
 
13.7%
other 35
 
0.5%

Most occurring characters

ValueCountFrequency (%)
i 18537
17.7%
t 12096
11.5%
r 12096
11.5%
n 11170
10.6%
e 6511
 
6.2%
_ 6476
 
6.2%
V 6476
 
6.2%
s 6476
 
6.2%
o 6476
 
6.2%
R 5585
 
5.3%
Other values (6) 13022
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85458
81.4%
Uppercase Letter 12987
 
12.4%
Connector Punctuation 6476
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 18537
21.7%
t 12096
14.2%
r 12096
14.2%
n 11170
13.1%
e 6511
 
7.6%
s 6476
 
7.6%
o 6476
 
7.6%
u 5585
 
6.5%
g 5585
 
6.5%
w 891
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
V 6476
49.9%
R 5585
43.0%
N 891
 
6.9%
O 35
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_ 6476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98445
93.8%
Common 6476
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 18537
18.8%
t 12096
12.3%
r 12096
12.3%
n 11170
11.3%
e 6511
 
6.6%
V 6476
 
6.6%
s 6476
 
6.6%
o 6476
 
6.6%
R 5585
 
5.7%
u 5585
 
5.7%
Other values (5) 7437
7.6%
Common
ValueCountFrequency (%)
_ 6476
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 18537
17.7%
t 12096
11.5%
r 12096
11.5%
n 11170
10.6%
e 6511
 
6.2%
_ 6476
 
6.2%
V 6476
 
6.2%
s 6476
 
6.2%
o 6476
 
6.2%
R 5585
 
5.3%
Other values (6) 13022
12.4%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing64
Missing (%)1.0%
Memory size51.7 KiB
Not Specified
2242 
Male
2167 
Female
2126 

Length

Max length13
Median length6
Mean length7.7383321
Min length4

Characters and Unicode

Total characters50570
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Specified
2nd rowNot Specified
3rd rowFemale
4th rowNot Specified
5th rowFemale

Common Values

ValueCountFrequency (%)
Not Specified 2242
34.0%
Male 2167
32.8%
Female 2126
32.2%
(Missing) 64
 
1.0%

Length

2023-01-22T00:07:53.059960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T00:07:53.156205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
not 2242
25.5%
specified 2242
25.5%
male 2167
24.7%
female 2126
24.2%

Most occurring characters

ValueCountFrequency (%)
e 10903
21.6%
i 4484
 
8.9%
a 4293
 
8.5%
l 4293
 
8.5%
N 2242
 
4.4%
o 2242
 
4.4%
t 2242
 
4.4%
2242
 
4.4%
S 2242
 
4.4%
p 2242
 
4.4%
Other values (6) 13145
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39551
78.2%
Uppercase Letter 8777
 
17.4%
Space Separator 2242
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10903
27.6%
i 4484
11.3%
a 4293
 
10.9%
l 4293
 
10.9%
o 2242
 
5.7%
t 2242
 
5.7%
p 2242
 
5.7%
c 2242
 
5.7%
f 2242
 
5.7%
d 2242
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
N 2242
25.5%
S 2242
25.5%
M 2167
24.7%
F 2126
24.2%
Space Separator
ValueCountFrequency (%)
2242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48328
95.6%
Common 2242
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10903
22.6%
i 4484
9.3%
a 4293
 
8.9%
l 4293
 
8.9%
N 2242
 
4.6%
o 2242
 
4.6%
t 2242
 
4.6%
S 2242
 
4.6%
p 2242
 
4.6%
c 2242
 
4.6%
Other values (5) 10903
22.6%
Common
ValueCountFrequency (%)
2242
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10903
21.6%
i 4484
 
8.9%
a 4293
 
8.5%
l 4293
 
8.5%
N 2242
 
4.4%
o 2242
 
4.4%
t 2242
 
4.4%
2242
 
4.4%
S 2242
 
4.4%
p 2242
 
4.4%
Other values (6) 13145
26.0%

Cookies Setting
Categorical

Distinct3
Distinct (%)< 0.1%
Missing62
Missing (%)0.9%
Memory size51.7 KiB
Required
2201 
ALL
2199 
Deny
2137 

Length

Max length8
Median length4
Mean length5.0104023
Min length3

Characters and Unicode

Total characters32753
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeny
2nd rowALL
3rd rowRequired
4th rowRequired
5th rowDeny

Common Values

ValueCountFrequency (%)
Required 2201
33.4%
ALL 2199
33.3%
Deny 2137
32.4%
(Missing) 62
 
0.9%

Length

2023-01-22T00:07:53.249173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T00:07:53.346089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
required 2201
33.7%
all 2199
33.6%
deny 2137
32.7%

Most occurring characters

ValueCountFrequency (%)
e 6539
20.0%
L 4398
13.4%
R 2201
 
6.7%
q 2201
 
6.7%
u 2201
 
6.7%
i 2201
 
6.7%
r 2201
 
6.7%
d 2201
 
6.7%
A 2199
 
6.7%
D 2137
 
6.5%
Other values (2) 4274
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21818
66.6%
Uppercase Letter 10935
33.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6539
30.0%
q 2201
 
10.1%
u 2201
 
10.1%
i 2201
 
10.1%
r 2201
 
10.1%
d 2201
 
10.1%
n 2137
 
9.8%
y 2137
 
9.8%
Uppercase Letter
ValueCountFrequency (%)
L 4398
40.2%
R 2201
20.1%
A 2199
20.1%
D 2137
19.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 32753
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6539
20.0%
L 4398
13.4%
R 2201
 
6.7%
q 2201
 
6.7%
u 2201
 
6.7%
i 2201
 
6.7%
r 2201
 
6.7%
d 2201
 
6.7%
A 2199
 
6.7%
D 2137
 
6.5%
Other values (2) 4274
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6539
20.0%
L 4398
13.4%
R 2201
 
6.7%
q 2201
 
6.7%
u 2201
 
6.7%
i 2201
 
6.7%
r 2201
 
6.7%
d 2201
 
6.7%
A 2199
 
6.7%
D 2137
 
6.5%
Other values (2) 4274
13.0%

Education
Categorical

Distinct4
Distinct (%)0.1%
Missing69
Missing (%)1.0%
Memory size51.7 KiB
Others
1646 
Graduate
1636 
Not Specified
1632 
Diploma
1616 

Length

Max length13
Median length8
Mean length8.4980092
Min length6

Characters and Unicode

Total characters55492
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Specified
2nd rowGraduate
3rd rowDiploma
4th rowGraduate
5th rowOthers

Common Values

ValueCountFrequency (%)
Others 1646
24.9%
Graduate 1636
24.8%
Not Specified 1632
24.7%
Diploma 1616
24.5%
(Missing) 69
 
1.0%

Length

2023-01-22T00:07:53.447073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T00:07:53.551767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
others 1646
20.2%
graduate 1636
20.0%
not 1632
20.0%
specified 1632
20.0%
diploma 1616
19.8%

Most occurring characters

ValueCountFrequency (%)
e 6546
 
11.8%
t 4914
 
8.9%
a 4888
 
8.8%
i 4880
 
8.8%
r 3282
 
5.9%
d 3268
 
5.9%
o 3248
 
5.9%
p 3248
 
5.9%
O 1646
 
3.0%
h 1646
 
3.0%
Other values (11) 17926
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45698
82.4%
Uppercase Letter 8162
 
14.7%
Space Separator 1632
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6546
14.3%
t 4914
10.8%
a 4888
10.7%
i 4880
10.7%
r 3282
7.2%
d 3268
7.2%
o 3248
 
7.1%
p 3248
 
7.1%
h 1646
 
3.6%
s 1646
 
3.6%
Other values (5) 8132
17.8%
Uppercase Letter
ValueCountFrequency (%)
O 1646
20.2%
G 1636
20.0%
N 1632
20.0%
S 1632
20.0%
D 1616
19.8%
Space Separator
ValueCountFrequency (%)
1632
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53860
97.1%
Common 1632
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6546
12.2%
t 4914
 
9.1%
a 4888
 
9.1%
i 4880
 
9.1%
r 3282
 
6.1%
d 3268
 
6.1%
o 3248
 
6.0%
p 3248
 
6.0%
O 1646
 
3.1%
h 1646
 
3.1%
Other values (10) 16294
30.3%
Common
ValueCountFrequency (%)
1632
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6546
 
11.8%
t 4914
 
8.9%
a 4888
 
8.8%
i 4880
 
8.8%
r 3282
 
5.9%
d 3268
 
5.9%
o 3248
 
5.9%
p 3248
 
5.9%
O 1646
 
3.0%
h 1646
 
3.0%
Other values (11) 17926
32.3%

Marital Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing47
Missing (%)0.7%
Memory size51.7 KiB
Other
2220 
Single
2170 
Married
2162 

Length

Max length7
Median length6
Mean length5.9911477
Min length5

Characters and Unicode

Total characters39254
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowOther
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Other 2220
33.6%
Single 2170
32.9%
Married 2162
32.8%
(Missing) 47
 
0.7%

Length

2023-01-22T00:07:53.656566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T00:07:53.759727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
other 2220
33.9%
single 2170
33.1%
married 2162
33.0%

Most occurring characters

ValueCountFrequency (%)
e 6552
16.7%
r 6544
16.7%
i 4332
11.0%
O 2220
 
5.7%
t 2220
 
5.7%
h 2220
 
5.7%
S 2170
 
5.5%
n 2170
 
5.5%
g 2170
 
5.5%
l 2170
 
5.5%
Other values (3) 6486
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32702
83.3%
Uppercase Letter 6552
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6552
20.0%
r 6544
20.0%
i 4332
13.2%
t 2220
 
6.8%
h 2220
 
6.8%
n 2170
 
6.6%
g 2170
 
6.6%
l 2170
 
6.6%
a 2162
 
6.6%
d 2162
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
O 2220
33.9%
S 2170
33.1%
M 2162
33.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39254
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6552
16.7%
r 6544
16.7%
i 4332
11.0%
O 2220
 
5.7%
t 2220
 
5.7%
h 2220
 
5.7%
S 2170
 
5.5%
n 2170
 
5.5%
g 2170
 
5.5%
l 2170
 
5.5%
Other values (3) 6486
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6552
16.7%
r 6544
16.7%
i 4332
11.0%
O 2220
 
5.7%
t 2220
 
5.7%
h 2220
 
5.7%
S 2170
 
5.5%
n 2170
 
5.5%
g 2170
 
5.5%
l 2170
 
5.5%
Other values (3) 6486
16.5%

WeekendPurchase
Categorical

Distinct2
Distinct (%)< 0.1%
Missing58
Missing (%)0.9%
Memory size51.7 KiB
0.0
5050 
1.0
1491 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters19623
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5050
76.5%
1.0 1491
 
22.6%
(Missing) 58
 
0.9%

Length

2023-01-22T00:07:53.847949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T00:07:53.939787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5050
77.2%
1.0 1491
 
22.8%

Most occurring characters

ValueCountFrequency (%)
0 11591
59.1%
. 6541
33.3%
1 1491
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13082
66.7%
Other Punctuation 6541
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11591
88.6%
1 1491
 
11.4%
Other Punctuation
ValueCountFrequency (%)
. 6541
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19623
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11591
59.1%
. 6541
33.3%
1 1491
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19623
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11591
59.1%
. 6541
33.3%
1 1491
 
7.6%

Interactions

2023-01-22T00:07:46.184491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:21.522314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:23.315878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:25.142392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:27.764054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:29.603099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:31.403291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:33.402687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:35.193697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:37.012196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:38.930102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:40.657046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:42.412354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:44.196399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-01-22T00:07:23.441139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:25.287382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:27.876436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:29.725277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:31.527961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:33.528124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:35.313925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:37.136822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:39.042428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:40.791878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:42.529964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:44.317087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:46.439223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:21.759385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:23.565402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:25.416040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:28.002280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:29.867765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:31.650717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:33.649041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:35.437423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:37.254605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:39.156125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:40.912329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:42.657601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:44.436519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:46.557557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:21.873306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:23.688743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:25.536543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:28.115309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:29.998135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:31.769716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:33.774974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:35.569112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:37.370900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:39.269284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:41.037550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:42.768988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:44.554657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:46.672484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:22.012290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:23.801011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:25.653889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:28.228904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:30.128459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:31.881749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:33.902781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:35.715695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:37.488925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:39.378846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:41.149514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:42.884635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:44.671787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-01-22T00:07:30.250815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:32.001133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-01-22T00:07:39.504960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-01-22T00:07:24.050493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:25.900945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:28.486307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-01-22T00:07:37.745113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:39.624849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:41.408278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:43.148624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-01-22T00:07:24.187522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-01-22T00:07:28.612346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:30.503271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:32.469967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:34.302213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:36.121083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:38.072762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:39.761028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:41.539521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:43.292491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:45.306975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:47.209004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:22.561014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:24.315112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:26.156310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:28.741284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:30.631211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:32.686250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:34.437441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:36.252166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:38.199510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:39.884692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:41.660509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:43.419720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:45.441597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:47.332745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:22.692781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:24.436728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:26.272928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:28.858469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:30.760072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:32.812247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:34.558503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:36.376779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:38.321053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:40.008387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:41.792625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:43.541076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:45.567176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:47.457598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:22.847515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:24.557374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:26.391152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:28.977910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:30.882628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:32.924563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:34.682050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:36.506742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:38.438223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:40.157464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:41.927668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:43.661235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:45.690081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:47.590729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:22.968459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:24.689627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:26.525820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:29.119777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:31.033640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:33.044700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:34.802551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:36.635628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:38.563815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:40.289715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:42.048896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:43.787610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:45.815216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:47.709088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:23.081141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:24.816058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:26.652965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:29.291502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:31.148950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:33.162139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:34.933635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:36.752255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:38.690539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:40.405066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:42.162463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:43.911435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:45.924589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:47.842698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:23.203337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:24.952718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:27.647414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:29.477580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:31.279312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:33.283155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:35.067757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:36.884295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:38.809732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:40.532587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:42.291393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:44.036607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T00:07:46.051451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-01-22T00:07:54.045665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-01-22T00:07:54.266913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-22T00:07:54.467666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-22T00:07:54.667657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-22T00:07:54.858501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-22T00:07:55.022261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-22T00:07:48.074693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-22T00:07:48.355865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-22T00:07:48.752060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

HomePageHomePage_DurationLandingPageLandingPage_DurationProductDescriptionPageProductDescriptionPage_DurationGoogleMetric:Bounce RatesGoogleMetric:Exit RatesGoogleMetric:Page ValuesSeasonalPurchaseMonth_SeasonalPurchaseOSSearchEngineZoneType of TrafficCustomerTypeGenderCookies SettingEducationMarital StatusWeekendPurchase
00.00.00.00.01.00.0000000.2000000.2000000.00.0Feb1.01.01.01.0Returning_VisitorNot SpecifiedDenyNot SpecifiedSingle0.0
10.00.00.00.02.064.0000000.0000000.1000000.00.0Feb2.02.01.02.0Returning_VisitorNot SpecifiedALLGraduateMarried0.0
20.00.00.00.019.0154.2166670.0157890.0245610.00.0Feb2.02.01.03.0Returning_VisitorFemaleRequiredDiplomaOther0.0
30.00.00.00.02.037.0000000.0000000.1000000.00.8Feb2.02.02.03.0Returning_VisitorNot SpecifiedRequiredGraduateOther0.0
40.00.00.00.016.0407.7500000.0187500.0258330.00.4Feb1.01.04.03.0Returning_VisitorFemaleDenyOthersOther0.0
50.00.00.00.03.0105.0000000.0000000.0333330.00.0Feb3.02.01.05.0Returning_VisitorMaleDenyDiplomaOther0.0
60.00.00.00.01.00.0000000.2000000.2000000.00.0Feb2.02.04.01.0Returning_VisitorFemaleALLOthersSingle1.0
70.00.00.00.04.076.0000000.0500000.1000000.00.0Feb1.01.01.03.0Returning_VisitorNot SpecifiedRequiredOthersMarried0.0
80.00.00.00.02.035.0000000.0000000.1000000.00.0Feb1.01.06.03.0Returning_VisitorFemaleDenyGraduateOther0.0
90.00.00.00.03.078.0000000.0000000.0666670.00.0Feb1.02.06.06.0Returning_VisitorMaleALLGraduateMarried1.0
HomePageHomePage_DurationLandingPageLandingPage_DurationProductDescriptionPageProductDescriptionPage_DurationGoogleMetric:Bounce RatesGoogleMetric:Exit RatesGoogleMetric:Page ValuesSeasonalPurchaseMonth_SeasonalPurchaseOSSearchEngineZoneType of TrafficCustomerTypeGenderCookies SettingEducationMarital StatusWeekendPurchase
65895.062.0000000.00.0040.0737.2619050.0133330.0288890.0000000.6May2.02.07.02.0Returning_VisitorMaleRequiredNot SpecifiedSingle0.0
65901.017.0000001.023.7562.03688.8468610.0166130.0271513.6637780.0Dec3.02.03.01.0Returning_VisitorMaleDenyGraduateSingle1.0
659113.0295.9285715.017.90154.06759.3119280.0057670.0288360.0000000.0Nov2.02.03.010.0Returning_VisitorMaleALLGraduateOther0.0
65920.00.0000000.00.001.00.0000000.2000000.2000000.0000000.0Mar3.02.01.01.0Returning_VisitorNot SpecifiedALLDiplomaOther0.0
65935.032.0000002.03.0033.01039.7666670.0000000.0202700.0000000.0Mar2.04.03.02.0Returning_VisitorNot SpecifiedDenyGraduateMarried1.0
65940.00.0000000.00.007.0208.0000000.0000000.0285710.0000000.0Feb4.01.01.05.0Returning_VisitorNot SpecifiedRequiredGraduateOther1.0
65950.00.0000003.044.00179.01738.4725290.0000270.0259980.0000000.0Aug2.04.09.011.0Returning_VisitorMaleRequiredNot SpecifiedOther0.0
65965.099.1666671.027.0033.0NaN0.0027780.0091270.0000000.6May8.05.01.02.0Returning_VisitorFemaleRequiredGraduateMarried0.0
65970.00.0000000.00.003.09.0000000.0666670.1333330.0000000.0May2.02.02.03.0Returning_VisitorNot SpecifiedDenyDiplomaOther1.0
65980.00.0000000.00.006.0313.0000000.0000000.0500000.0000000.0May2.04.03.01.0Returning_VisitorMaleRequiredDiplomaSingle0.0

Duplicate rows

Most frequently occurring

HomePageHomePage_DurationLandingPageLandingPage_DurationProductDescriptionPageProductDescriptionPage_DurationGoogleMetric:Bounce RatesGoogleMetric:Exit RatesGoogleMetric:Page ValuesSeasonalPurchaseMonth_SeasonalPurchaseOSSearchEngineZoneType of TrafficCustomerTypeGenderCookies SettingEducationMarital StatusWeekendPurchase# duplicates
00.00.00.00.01.00.00.20.20.00.0Dec1.01.01.03.0New_VisitorMaleDenyOthersMarried0.02
10.00.00.00.01.00.00.20.20.00.0Dec2.02.01.02.0Returning_VisitorNot SpecifiedNaNDiplomaOther0.02
20.00.00.00.01.00.00.20.20.00.0Dec2.02.02.03.0Returning_VisitorFemaleRequiredDiplomaSingle0.02
30.00.00.00.01.00.00.20.20.00.0Dec2.04.01.01.0Returning_VisitorNot SpecifiedRequiredNot SpecifiedSingle1.02
40.00.00.00.01.00.00.20.20.00.0Dec3.02.07.01.0Returning_VisitorMaleDenyDiplomaSingle0.02
50.00.00.00.01.00.00.20.20.00.0Dec3.03.01.08.0New_VisitorNot SpecifiedDenyOthersSingle0.02
60.00.00.00.01.00.00.20.20.00.0Jul3.02.01.04.0Returning_VisitorMaleDenyDiplomaOther1.02
70.00.00.00.01.00.00.20.20.00.0Mar1.01.08.01.0Returning_VisitorMaleALLOthersMarried0.02
80.00.00.00.01.00.00.20.20.00.0Mar2.05.01.03.0Returning_VisitorFemaleALLOthersMarried0.02
90.00.00.00.01.00.00.20.20.00.0May1.01.02.02.0Returning_VisitorNot SpecifiedDenyOthersSingle0.02